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2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)最新文献

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Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning 基于深度学习的二维无标签显微图像实例分割
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600522
B. A. Mohamed, Lamees N. Mahmoud, W. Al-Atabany, N. Salem
The precise detection and segmentation of cells in microscopic image sequences is an essential task in biomedical research, such as drug discovery and studying the development of tissues, organs, or entire organisms. However, the detection and segmentation of cells in phase contrast images with a halo and shade-off effects is still challenging. Lately, Mask Regional Convolutional Neural Network (Mask R-CNN) has been introduced for object detection and instance segmentation of natural images. This study investigates the efficacy of the Mask R-CNN to instantly detect and segment label-free microscopic images. The dataset used in this paper is taken from the ISBI cell tracking challenge. The Mask R-CNN is trained using different hyperparameters and compared to the U-Net model. Experimental results show that the Mask R-CNN model achieves 91.6 % when using ResNet-50 backbone and COCO weights.
显微图像序列中细胞的精确检测和分割是生物医学研究中的一项重要任务,例如药物发现和研究组织、器官或整个生物体的发育。然而,具有晕晕和阴影效果的相衬图像中细胞的检测和分割仍然是一个挑战。近年来,Mask区域卷积神经网络(Mask R-CNN)被引入到自然图像的目标检测和实例分割中。本研究探讨了Mask R-CNN在即时检测和分割无标签显微图像方面的功效。本文使用的数据集来自ISBI单元跟踪挑战。Mask R-CNN使用不同的超参数进行训练,并与U-Net模型进行比较。实验结果表明,当使用ResNet-50主干网和COCO权值时,Mask R-CNN模型的识别率达到91.6%。
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引用次数: 0
ESLDL: An Integrated Deep Learning Model for Egyptian Sign Language Recognition ESLDL:埃及手语识别的集成深度学习模型
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600492
Soha Ahmed Ehssan Aly, Aya Hassanin, Saddam Bekhet
Sign languages is a critical requirement that helps deaf people to express their needs, feelings and emotions using a variety of hand gestures throughout their daily life. This language had evolved in parallel with spoken languages, however, it do not resemble its counterparts in the same way. Moreover, it is as complex as any other spoken language, as each sign language embodies hundreds of signs, that differs from the next by slight changes in hand shape, position, motion direction, face and body parts contributing to each sign. Unfortunately, sign languages are not globally standardized, where the language differs between countries and has its own vocabulary and varies although they might look similar. Furthermore, publicly available datasets are limited in quality and most of the available translation services are expensive, due to the required skilled human personnel. This paper proposes a deep learning approach for sign language detection that is finely tailored for the Egyptian sign language (special case of the generic sign language). The model is built to harnesses the power of convolutional and recurrent networks by integrating them together to better recognize the sign language spatio-temporal data-feed. In addition, the paper proposes the first Egyptian sign language dataset for emotion words and pronouns. The experimental results demonstrated the proposed approach promising results on the introduced dataset using combined CNN with RNN models.
手语是帮助聋哑人在日常生活中使用各种手势表达他们的需求、感受和情感的一项关键要求。这种语言是与口语并行发展的,然而,它并不以同样的方式与口语相似。此外,手语和其他口语一样复杂,因为每种手语都包含数百个手势,而每个手势的手部形状、位置、运动方向、面部和身体部位的细微变化都与下一个手势不同。不幸的是,手语并不是全球标准化的,各国之间的语言不同,有自己的词汇,尽管看起来很相似,但也有所不同。此外,公开可用的数据集质量有限,而且由于需要熟练的人力,大多数可用的翻译服务都很昂贵。本文提出了一种用于手语检测的深度学习方法,该方法为埃及手语(通用手语的特殊情况)量身定制。该模型的建立是为了利用卷积和循环网络的力量,通过将它们集成在一起来更好地识别手语的时空数据馈送。此外,本文还提出了第一个埃及手语情感词和代词数据集。实验结果表明,该方法将CNN与RNN模型相结合,在引入的数据集上取得了良好的效果。
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引用次数: 0
Online Constraints Update Using Machine Learning for Accelerating Hardware Verification
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600485
Mostafa AboelMaged, M. Mashaly, M. A. E. Ghany
The evolution of computer systems and application-specific integrated circuits led to an increase in their complexity. Consequently, verification is a vital procedure in the design process to ensure correct functionality of the designs. However, the increase in the design's complexity led to the increase in the cost and time needed for the verification in the design process. Thus, to decrease the verification process time and cost, and achieve the best coverage for the design under test; machine learning techniques are used. In this paper, a verification environment that utilizes constrained random verification technique is introduced. The environment uses dynamic reseeding and rewinding techniques. The environment is also integrated with machine learning algorithms as well to update the constraint at run time to speed up the time needed to reach full design coverage. The environment can utilize previous simulations data or prior knowledge of the design to train the model. The environment uses a different neural network topology than the state of the art. The proposed environment recorded a decrease of 83.5% in the time needed and about 60000 times decrease in the error rate for training the machine learning algorithm in comparison with the state of the art.
计算机系统和专用集成电路的发展导致其复杂性的增加。因此,验证是设计过程中确保设计正确功能的重要步骤。然而,设计复杂性的增加导致了设计过程中验证所需的成本和时间的增加。从而减少验证过程的时间和成本,实现对被测设计的最佳覆盖;使用机器学习技术。本文介绍了一种利用约束随机验证技术的验证环境。该环境使用动态播种和倒带技术。该环境还集成了机器学习算法,以便在运行时更新约束,以加快达到完全设计覆盖所需的时间。环境可以利用以前的模拟数据或先前的设计知识来训练模型。这种环境使用的神经网络拓扑与目前的技术水平不同。与最先进的环境相比,所提出的环境所需时间减少了83.5%,训练机器学习算法的错误率减少了约60000倍。
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引用次数: 0
Diabetes Prediction Using Machine Learning: A Comparative Study 使用机器学习预测糖尿病:一项比较研究
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600091
Mohamed Rady, Kareem Moussa, Mahmoud Mostafa, Abdelrahman Elbasry, Zeyad Ezzat, Walaa Medhat
Diabetes is a common, metabolic disease, that results in a high level of blood sugar. Patients diagnosed with diabetes suffer from a body that cannot effectively use the insulin or cannot produce a sufficient amount of insulin. Providing a method of detection via symptoms that can be noticed by the patient can prompt the patient to seek medical assistance more promptly and in turn to be correctly diagnosed and treated. This paper proposed a solution for the problem using machine learning techniques. We applied eight algorithms on a data set of 521 subjects. The results are compared to each other to find the best algorithm for this task. The algorithms used are from different families which are logistic regression, support vector machines-linear and nonlinear kernel, random forest, decision tree, adaptive boosting classifier, K-nearest neighbor, and naïve bayes. The results show a clear advantage of using Random Forest with an accuracy of 98% having used 80% of the dataset for training and 20% for testing.
糖尿病是一种常见的代谢性疾病,会导致高血糖。被诊断为糖尿病患者的身体不能有效地使用胰岛素或不能产生足够数量的胰岛素。提供一种通过患者可以注意到的症状进行检测的方法,可以促使患者更及时地寻求医疗援助,从而得到正确的诊断和治疗。本文提出了一种利用机器学习技术解决该问题的方法。我们对521名受试者的数据集应用了8种算法。将结果相互比较,以找到该任务的最佳算法。使用的算法来自不同的家族,包括逻辑回归,支持向量机-线性和非线性核,随机森林,决策树,自适应增强分类器,k近邻和naïve贝叶斯。结果显示了使用随机森林的明显优势,使用80%的数据集进行训练,使用20%的数据集进行测试,准确率达到98%。
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引用次数: 4
Design of a highly-efficient embedded controller for AUV stabilization and trajectory tracking using minimal computational resources 利用最小的计算资源设计一种高效的水下航行器稳定和轨迹跟踪嵌入式控制器
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600509
S. A. Mohamed, Omar K. Abdelgelil, Osama A. Elhout, Hend M. Aafia, M. Awad, Hossam E. Abd El Munim
This paper proposes a computationally-efficient controller for an AUV which could be implemented using a single-purpose microcontroller. The AUV under study has a complex eight-thruster mechanical configuration. Such system imposes concerns like non-linear behavior, coupled dynamics and parameter uncertainty. An extensive study on vehicle kinematics/dynamics is proposed, followed by formulating a non-linear model for the test vehicle. Dynamic decoupling is applied to break the system into two sub-systems controlled using two independent simple controllers. An LQR controller is used for stabilizing vehicle depth and roll/pitch attitude. A self-tuning PID controller is used for trajectory tracking of surge velocity and yaw attitude. The combined LQR/Adaptive PID control architecture deals very well with noise and uncertainty with minimal computational effort. The controller is verified experimentally using multiple motion scenarios for a test AUV.
本文提出了一种计算效率高的AUV控制器,该控制器可以使用单一用途的微控制器来实现。正在研究的AUV具有复杂的八推进器机械结构。这样的系统带来了非线性行为、耦合动力学和参数不确定性等问题。提出了对车辆运动学/动力学的广泛研究,然后建立了测试车辆的非线性模型。采用动态解耦的方法将系统分解为两个独立的简单控制器控制的两个子系统。LQR控制器用于稳定车辆深度和侧倾/俯仰姿态。采用自整定PID控制器对浪涌速度和偏航姿态进行轨迹跟踪。结合LQR/自适应PID控制体系结构,以最小的计算量处理噪声和不确定性。该控制器在测试AUV的多个运动场景中进行了实验验证。
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引用次数: 0
Two-Terminal Perovskite/Silicon Solar Cell: Simulation and Analysis 双端钙钛矿/硅太阳能电池:模拟与分析
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600501
M. Mousa, F. Amer, Ahmed Saeed, Roaa I. Mubarak
Single junction solar cells have a limitation of absorbing part of the incident spectrum with energy photons lower than the energy gap of the used material, and even the photons with higher energies will generate electron-hole pairs, but the energy difference will be converted into thermalization loss. This problem can be solved by using tandem (multi-junction) solar cells. This work presents a proposed two-terminal Perovskite/Silicon tandem solar cell with 27.69% efficiency. Testing of the tandem cell performance with temperature is also presented.
单结太阳能电池的局限性是吸收了能量光子低于所用材料能隙的部分入射光谱,即使是能量更高的光子也会产生电子-空穴对,但能量差会转化为热化损失。这个问题可以通过使用串联(多结)太阳能电池来解决。本文提出了一种效率为27.69%的双端钙钛矿/硅串联太阳能电池。并对串联电池的性能随温度变化进行了测试。
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引用次数: 3
An Efficient Microcontroller for Visual Cortical Prosthesis 一种高效的视觉皮质假体微控制器
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600530
Nabeel Fattah
Medical implant devices (MID) are becoming increasingly used as a means of treating human disorders. Among such MIDs, the visual cortical prosthesis (VCP) is one of the treatments for blind people who want to restore their vision. The visual cortex stimulator (VCS) is a device that requires a reliable power source as well as quick data transfer. Furthermore, the data processing unit must meet specific criteria, such as low power consumption, small size, and efficient processing. One of the most efficient microcontrollers on the market is mentioned in this report. Their characteristics and capabilities are evaluated so that we may select an effective microcontroller that complies with all VCP requirements, including safety and health. The chosen microcontroller (ARM-Cortex M4) was mounted on a single round form printed circuit board (PCB) with a diameter of 30 mm and a thickness of 2.68 mm, together with other necessary components. Furthermore, utilizing low-power Bluetooth, a data transfer speed of 170 Kbps was achieved. Moreover, image decompression time was only 19.55 ms with an overall system power consumption of 80 mW.
医疗植入装置(MID)越来越多地被用作治疗人类疾病的手段。其中,视觉皮质假体(visual cortical prosthesis, VCP)是盲人恢复视力的治疗方法之一。视觉皮层刺激器(VCS)是一种需要可靠电源和快速数据传输的设备。此外,数据处理单元还必须满足特定的要求,如低功耗、小体积、高效处理等。本报告中提到了市场上最有效的微控制器之一。对它们的特性和功能进行评估,以便我们可以选择符合所有VCP要求的有效微控制器,包括安全和健康。所选择的微控制器(ARM-Cortex M4)与其他必要的组件一起安装在直径为30 mm,厚度为2.68 mm的圆形印刷电路板(PCB)上。此外,利用低功耗蓝牙,数据传输速度达到170 Kbps。此外,图像解压缩时间仅为19.55 ms,系统总功耗为80 mW。
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引用次数: 0
Robust Background Template for Saliency Detection 鲁棒背景模板显著性检测
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600546
Abram W. Makram, N. Salem, M. El-Wakad, W. Al-Atabany
In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse representation. Finally, the Bayesian framework used to integrate the two saliency maps to obtain the final one. Experimental results show that the proposed method performs favorably against eight state-of-the-art methods. In addition, the proposed method is shown to be more effective in highlighting the challenging salient objects that touch the image boundary.
本文针对优化后的背景模板,提出了一种有效的基于密集稀疏表示的显著性检测方法。首先,将输入图像划分为紧凑和均匀的超像素;然后,通过引入边界电导率测量来生成优化背景模板,以改进图像超像素在优化背景中的密集和稀疏表示,其中重建误差表示显著性度量。在优化模板的基础上,通过密集和稀疏表示生成两个显著性映射。最后,利用贝叶斯框架对两个显著性图进行整合,得到最终的显著性图。实验结果表明,该方法与八种最先进的方法相比具有良好的性能。此外,该方法在突出图像边界上具有挑战性的突出物体方面更为有效。
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引用次数: 0
Intelligent Arabic-Based Healthcare Assistant 智能阿拉伯医疗保健助手
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600526
Tasneem Wael, Ahmed Hesham, Mohamed Youssef, Omar Adel, Hamis Hesham, M. Darweesh
Text classification has been one of the most common natural language processing (NLP) objectives in recent years. Compared to other languages, this mission with Arabic is relatively restricted and in its early stages, and this combination in the medical application area is rare. This paper builds an Arabic health care assistant, specifically a pediatrician that supports Arabic dialects, especially Egyptian accents. The proposed application is a chatbot based on Artificial Intelligence (AI) models after experimenting with Two Bidirectional Encoder Representations from Transformers (BERT) models, a pre-trained BERT and Logistic regression TF-IDF and Doc2vec. These models were applied to the Arabic dataset with different dialects from different couturiers such as Egypt, Saudi Arabia, and Iraq. The proposed system consists of 4 stages: scrapping and collecting data, then wrangling it, data preprocessing, data extraction, trained models with new data, and connect the model to the database that contains the answers. Experimental tests showed that the BERT model outperformed the others by getting a 95% Accuracy. Logistic regression with Doc2vec was the second best with 71% F-measure and 73% Accuracy.
文本分类是近年来自然语言处理(NLP)中最常见的目标之一。与其他语文相比,使用阿拉伯文的特派团相对受限,而且还处于初期阶段,这种结合在医疗应用领域是罕见的。本文构建了一个阿拉伯语卫生保健助理,特别是一个支持阿拉伯语方言,特别是埃及口音的儿科医生。提出的应用程序是一个基于人工智能(AI)模型的聊天机器人,实验了来自变形金刚(BERT)模型的两个双向编码器表示,一个预训练的BERT和逻辑回归TF-IDF和Doc2vec。这些模型被应用于阿拉伯语数据集,这些数据集具有来自埃及、沙特阿拉伯和伊拉克等不同时装设计师的不同方言。提出的系统包括4个阶段:废弃和收集数据,然后整理数据,数据预处理,数据提取,用新数据训练模型,并将模型连接到包含答案的数据库。实验测试表明,BERT模型的准确率达到95%,优于其他模型。Doc2vec的Logistic回归是第二好的,f测量值为71%,准确度为73%。
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引用次数: 3
Precise Orientation Estimation Based on Nonlinear Modeling and Quaternion Transformations for Low Cost Navigation Systems 基于非线性建模和四元数变换的低成本导航系统精确方位估计
Pub Date : 2021-10-23 DOI: 10.1109/NILES53778.2021.9600089
Mohamed S. Elsabbagh, A. H. Hassaballa, A. Kamel, Y. Elhalwagy
An adaptive unscented Kalman filter (AUKF) is designed to estimate the roll and pitch angles of rigid body using low-cost inertial sensors. The main challenge is concerned about the tilt orientation estimation in high dynamic environments, where the linear acceleration affects the orientation accuracy. The proposed filter is based on the quaternion technique and an additive function which is used to compensate the influence of accelerometer corrections during motions. The algorithm is implemented on a STM32F407 ARM Cortex-M4 series microcontrollers and fused three-axis accelerometer and, three single-axis gyroscopes triads based on micro-electromechanical system (MEMS) technology. The experimental and field tests results analysis showed an outstanding real-time navigation performance when compared with the traditional KF and other commercial expensive systems.
设计了一种自适应无气味卡尔曼滤波(AUKF),利用低成本惯性传感器估计刚体的横滚角和俯仰角。主要的挑战是在高动态环境下的倾斜方向估计,其中线性加速度会影响方向精度。该滤波器基于四元数技术和用于补偿运动过程中加速度计修正影响的加性函数。该算法在STM32F407 ARM Cortex-M4系列微控制器和基于微机电系统(MEMS)技术的融合三轴加速度计和三个单轴陀螺仪三联上实现。实验和现场测试结果分析表明,与传统KF和其他昂贵的商用系统相比,该系统具有出色的实时导航性能。
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引用次数: 1
期刊
2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)
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